Using Data Science to Improve Your Product and Marketing- An Airbnb Case Study

What is data science, and how does Airbnb effectively utilize it in its product and marketing strategy? Let’s find out!

Airbnb has continued to dominate the hospitality and travel industry. Within a relatively short time, the company has grown tremendously.

What started with the founders providing lodging for 3 guests in their living room has become a multi-billion-dollar brand today.

How did they grow so quickly, especially at a time when a lot of start-ups were struggling to break even?

Thankfully, Airbnb hasn’t kept its success formula hidden.

They have repeatedly credited their success to how they leverage data science on a large scale. 

In this article, we will uncover the answers to these questions and conclude by seeing how you and your team, like Airbnb, can effectively apply these ingredients to your product and marketing strategy in order to achieve success…

Sounds like a worthwhile adventure, right? Let’s dive right in.

WHAT IS DATA SCIENCE?

Wikipedia defines data science as “an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured and unstructured data and apply knowledge from data across a broad range of domains.”

What does this even mean?

This is no rocket-science-inspired definition; in its basic form, it is the use of analytical and scientific methods to extract knowledge or useful information from “dirty data.”

Businesses use the knowledge garnered from data science to solve a problem or improve an existing solution.

Let’s see how Airbnb did this.

HOW DOES AIRBNB EFFECTIVELY LEVERAGE DATA SCIENCE?

1. They look to data as indicative of the customers’ needs.

The former head of data at Airbnb, Riley Newman, said that Airbnb considers “data as the voice of the customer and data science as the interpretation of that voice.”

At Airbnb, the in-house data scientists and analysts actively seek out and collaborate with other stakeholders such as marketers, designers, product managers, etc.

This helps to create an informed outlook of their user data and assists the team in understanding and interpreting that “voice of the customer” properly.

2. They use data to improve their search feature.

The powerhouse of the Airbnb site is a search feature that’s designed to provide the ultimate user experience. 

In its early days before its data-science-driven evolution, Airbnb wasn’t so sure how to tailor recommendations to users based on location.  

Back then, they just went along with a model which returned the highest quality listings within a certain radius according to the user’s set location.

A couple of years later, Airbnb overhauled the search feature and implemented a smarter, more data-driven one.

Why?

They let their customers solve the problem for them.

In time, their customer base increased, and their websites got more visitors, leading to tons of historical data for data science and machine learning processes to rely on and learn from.

How?

Riley Newman explained that they used a rich dataset made-up of guest and host interactions to build a model that estimated a conditional probability of booking in a location where the user searched.

For example, a search for “San Francisco” would automatically skew toward neighborhoods where people who search for San Francisco end up booking.

3. Using data science to ascertain what the Airbnb hosts prefer.

Airbnb was looking to match folks looking for accommodations with those wanting to rent out their spaces, so they had to find a way to reconcile accommodation requests with host preferences. 

In a quest to find a perfect fit for this reconciliation, Airbnb made use of a machine learning algorithm to create an application that uses host and guest preferences to personalize results.

This ensured more accuracy and a higher margin of successful matches between guests and hosts.

4. They measure user experience.

Airbnb measures user experience using the Net Promoter Score (NPS) in tandem with other customer success metrics.

 This assists in inferring and making predictions on when and why customers are likely to recommend.

5. Use of Split testing at every stage of development.

Airbnb’s business model is a lot more complicated and complex than traditional ones.

At every stage of AirBnB’s development, the company must consider both hosts and guests.

To meet this challenge, Airbnb developed using a split tests approach and its A/B testing framework, taking care to note how changes affect various user groups.

6. They continue to look for opportunities to get better and explore new frontiers.

Perhaps one of the most remarkable things about Airbnb is the willingness to constantly use data to improve itself.

Recently, they launched the Smart Pricing tool. This tool leverages advanced machine learning, pulling information from 5 billion training points whilst still considering personal inputs to help hosts set the perfect price. 

They also launched Aerosolve, a learning system based on open-source ML that notices patterns and attempts to see why certain listings command higher prices.

KEY TAKEAWAYS.

What key takeaways do we get from this? What should these insights remind and motivate you to do?

Here are a few we notice:

1. Embrace data science and dig deeper into the data available when faced with a problem that regular tests cannot provide a solution to. 

2. Keep learning by listening to data and adapting your product and marketing in line with the insights gathered through data science. 

3. Recognize the value of collecting data. Without a doubt, data has become invaluable to modern businesses. 

Paying attention to and collecting data becomes very important to the success of your business. 

It wouldn’t be out of place to invest in data collecting and storage solutions like Snowflake. 

Snowflake is a cloud-based data warehouse built on top of AWS or Microsoft Azure cloud infrastructure; it offers scalable storage and computing to meet the needs of growing businesses.

In essence, let your decisions be data-driven. 

You know what else rhymes with data-driven?

Datameer! – A Saas tool that enables data engineers and analysts to transform and model data directly in their cloud warehouses via a simple SQL code or no-code interface.

Deploy Snowflake and try out our free Datameer trial today!